• We will be examining the response times of several “official” Twitter accounts: CalFire, Butte County Sheriff, and Chico Fire Department.
• Why is this important?
  • Because of the need for accurate, timely warnings and other information should a disaster, natural or otherwise, occur and threaten the lives and livelihoods of people.
• What about using television? Phone calls? Radio?
• How reliable is this information?
• What if you want more information?
• That is why it is also very important to examine the times it takes for officials to issue official information and warnings to the public, such as evacuation notices, which can literally be life-saving information during disasters.

• 20% television viewership decline between 2014-15 and 2018-19 (28.5 million viewers in PRIMETIME).
• A third of all US households have no radio at home (Radio Ink, 2020).
• Over reliance on phone calls or texts for emergency information (St. John, Paige, and Joseph Serna, 2018)
• Twitter has an audience of 64.2 million users.
  • Social media is slowly replacing traditional media outlets in terms of news distribution.
    • News now usually appears on social media first.
    • Social media crosses geographic boundaries more quickly.
    • Social channels are informed by multiple individuals firsthand, often through unofficial sources, but sometimes those who are direct witnesses to events.
    • Social media is commented upon by active social media users who further share and distribute content.
• Social media provides egalitarian access to the production and the consumption of news (Ceron, 2014).
• 77% of journalists say social media is important to them for learning about potential stories faster, and that it is important in reporting stories faster (Elliott 2018).
• During November of 2018 alone, CalFire’s official Twitter account gained about 35k followers, which shows them to be an important source of information for the public (SocialBlade, 2020).

• Disaster Response and Twitter
  • Why is Twitter data important during disasters?
    • Gives a “real-time view” of disasters, allows fast-flowing information quickly from official sources (Ford, 2018).
  • There is a need for unique updates across organizations given the volume of data produced because manually managing is not always practical right after a disaster (Ford, 2018).
  • New programs tailored towards combing Twitter during disasters for noun-verb pairs like “bridge collapsed” and “person trapped”. This helps official response times in future disasters (Ford, 2018).
• Potential Flaws in Phone-Based Warning Systems
  • Butte County Sheriff’s office showed that the first evacuation orders requested by firefighters failed to connect. Half of the calls went to voicemail (St. John and Serna, 2018).”
  • This inefficiency presents a real problem within our system.
  • The police chief of Paradise said “there was no time for a citywide evacuation order — the city’s own system went down in the midst of a partial order” (St. John and Serna, 2018). With California having the most Twitter users, there needs to be a wide array of emergency notification methods through Twitter (Stirtz, 2020).

• Refiguration of disaster social relations
  • New communication technologies replacing traditional media, transformation of disaster visibility (Cottle 2014).
  • Individuals are central in emergency responses (Cottle 2014).
  • “Challenging the top-down, elite dominated communications” (Cottle 2014)
  • Civilian surge of information and worldwide engagement because of fast-flowing information, collaboration between citizens, disaster relief agencies, volunteers, and public figures (Cottle 2014).
• Limits to crisis data
  • When data collection starts, social media platforms can create analytical and ethical oversights (limited access to the internet during a disaster: power outages, etc) (Crawford and Finn 2015).
  • Traditional media was state-controlled, but that changed in more recent years (Crawford and Finn 2015).
  • The Twitter algorithm influences the way tweets are created or how they are retweeted; “bots” can also impede data-collection (Crawford and Finn 2015).
  • People cannot control how their data is used: privacy issue (Crawford and Finn 2015).

• How timely were public officials and emergency services twitter accounts in disseminating important and crucial information to the public during the Camp Fire? We will create a timeline of events to illustrate this problem.
• Most research done regarding social media and its innovative uses in emergency management revolves around incidents such as earthquakes (only a sixth of cases pertained to wildfires).
• Our main goal is to examine the timeliness of social media in emergency management by examining the response times of specific accounts along the timeline of the Camp Fire. Because the first few hours of a disaster are the most important for sending out emergency information, we will limit our focus accordingly.
• We will use a timeline to examine these response times more closely and compare them to the timeline of the actual disaster.

• 72,748 tweets were collected using the rtweet (Kearney MW 2019) package using R 3.6.2 (R Core Team) beginning the morning of November 11, 2018 by the Spring 2020 Advanced Data Science class at CSU, Chico. Tweets were collected Using the query: #CampFire, #campfire, #paradise, #Paradise or @CALFIRE_ButteCo"
• A timeline of events from the morning of November 8, 2018 was derived from the Chico Enterprise-Record. (Epley)
• We are observing tweets regarding seven public figures’ and organizations’ twitter accounts that were instrumental during the Camp Fire, specifically: @Cal_Fire, @CalFire_ButteCo, @ButteSherriff, @ChicoFD, @CountyOfButte, @Paradise_CA, and @ChicoPolice.
  • Data from these accounts are defined as “media” while all others are defined as “public”

• Variable Creation: To successfully compare the response time of the local media accounts to the real life timing of events we populated a data frame with each event and time as specified by Chico Enterprise-Record’s timeline of the morning of the fire (Epley).
• Data Analysis: We analyzed the specific timing of emergency services and government entities’ tweets regarding pertinent information in relationship to the real life events through a timeline that incorporates both the twitter data and the derived timeline data in one merged timeline plot.
  • We additionally created a timeline documenting the comparison between times of general public tweets and tweets from our local media sources.

• We were most interested in initial response times data, so we filtered down the dataset to only include tweets that occurred during the first six hours from the fire starting.
• Each record in the data represents an individual tweet’s metadata. The variables from the metadata used throughout include:
  • Tweet content
  • User’s Twitter Handle
  • Whether the tweet was a retweet
  • Time the tweet was created
  • The events derived from Chico Enterprise-Record’s timeline (Epley)

While the original dataset includes 74,099 tweets, we are only concerned with the tweets that occur within the first 6 hours of the fire starting and those that directly address the fire. This ends up being just 446 tweets, which amounts to 0.61% of the overall data.

In the following timeline each dot signifies a tweet or event to show tweet times in comparison to the real event times. The majority of the tweets shown are evacuation warnings and orders.

After eliminating retweets the following timeline compares the tweet frequency of the public vs the media for the morning up until 10:00 AM.

• Our results show that Twitter can be used to disseminate important and potentially life saving information in a timely manner, as was seen on the timeline of events for the Camp Fire.
• From our Twitter data, we can also see that Twitter can provide individuals with this information in a consistent manner, when other media methods may be ineffective in sharing this data (such as losing power during a disaster and being unable to receive news via television).
• This ultimately means that social media platforms such as Twitter, if used properly, can be an important tool when it comes to emergency alert systems.

• In these changing times, especially with social media developing to be one of the essential means of communication, it is important to examine the place of Twitter in these new developments. Our results show that when official accounts respond to events promptly, it gives the organization or person more credibility, especially in the context of natural disasters where people count on them to stay safe and informed on the changing situation.
• With a mounting number of people using Twitter, data collected from social media is useful in offering insight into public opinion. It makes it easier to overcome public misperception and helps different actors in responding to crises in a way that benefits the collective.
• When it comes to crisis communications, while other forms of communication such as email, television, phone calls, or websites can be impractical in informing the public, social media offers a more direct connection that facilitates communication. In the case of Twitter, retweets permeate information at a rapid pace, which enables a message to be communicated to a large number, of people, which can potentially limit disaster.

Ceron, Andrea. (2014). Twitter and the Traditional Media: Who is the Real Agenda Setter?. APSA 2014 Annual Meeting Paper

Cottle, Simon. (2014). Rethinking Media and Disasters in a Global Age: What’s Changed and Why It Matters. Media, War & Conflict. 7. 3-22.10.1177/1750635213513229. https://www.researchgate.net/publication/270633894_Rethinking_Media_and_Disasters_in_a_Global_Age_What's_Changed_and_Why_It_Matters

Crawford, Kate and Finn, Megan. (2015). The limits of crisis data: analytical and ethical challenges of using social and mobile data to understand disasters. GeoJournal. 80. 491-502 https://www.researchgate.net/publication/285401858_The_limits_of_crisis_data_analytical_and_ethical_challenges_of_using_social_and_mobile_data_to_understand_disasters

Elliott, Jennifer. “8 Best Practices for Emergency Communications on Social Media.” EfficientGov, 19 July 2018, https://www.efficientgov.com/community-engagement/articles/8-best-practices-for-emergency-communications-on-social-media-vwZS7OO5eoblrs3G/.

Epley, Robin (November 8, 2019). “Timeline: Breaking down Nov. 8 - the Day the Camp Fire Sparked.” Chico Enterprise-Record, Chico Enterprise-Record https://www.chicoer.com/2019/11/07/timeline-breaking-down-nov-8-the-day-the-camp-fire-sparked/.

Ford, Jordan. “Improving Disaster Response through Twitter Data.” Penn State University, 2018, https://news.psu.edu/story/527730/2018/07/10/research/improving-disaster-response-through-twitter-data.

Kearney MW (2019). “rtweet: Collecting and analyzing Twitter data.” Journal of Open Source Software, 4(42), 1829. Doi: 10.21105/joss.01829, R package version 0.7.0, https://joss.theoj.org/papers/10.21105/joss.01829.

R Core Team (2019). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org.

Radio Ink (March 3, 2020). “The Decline of the Home Radio”. https://radioink.com/2020/03/03/the-decline-of-the-home-radio/

Rice, Doyle (January 8, 2019). “USA had world’s 3 costliest natural disasters in 2018, and Camp Fire was the worst”. USA Today.

Social Blade. “Cal Fire’s Twitter Stats Summary Profile.” Social Blade, 2020, socialblade.com/twitter/user/cal_fire.

St. John, Paige, and Joseph Serna. “Camp Fire Evacuation Warnings Failed to Reach More than a Third of Residents Meant to Receive Calls.” Los Angeles Times, 1 Dec. 2018, https://www.latimes.com/local/california/la-me-ln-paradise-evacuation-warnings-20181130-story.html.

Stirtz, Kevin. “Twitter Ranking: Which States Twitter the Most?” All Business, Dun & Bradstreet, 14 May 2009, https://www.allbusiness.com/twitter-ranking-which-states-twitter-the-most-12329567-1.html.